Systems and methods for optimizing geometric stretch of a parametrization scheme

ABSTRACT

Systems and methods are provided for optimizing the geometric stretch of a parametrization scheme. Given an arbitrary mesh, the systems and methods construct a progressive mesh (PM) such that all meshes in the PM sequence share a common texture parametrization. The systems and methods minimize geometric stretch, i.e., small texture distances mapped onto large surface distances, to balance sampling rates over all locations and directions on the surface. The systems and methods also minimize texture deviation, i.e., “slippage” error based on parametric correspondence, to obtain accurate textured mesh approximations. The technique(s) begin by partitioning the mesh into charts using planarity and compactness heuristics. Then, the technique(s) proceed by creating a stretch-minimizing parametrization within each chart, and by resizing the charts based on the resulting stretch. Then, the technique(s) simplify the mesh while respecting the chart boundaries. Next, the parametrization is re-optimized to reduce both stretch and deviation over the whole PM sequence. The charts may then be packed into a texture atlas for improved texture mapping in connection with a parametrization scheme.

COPYRIGHT NOTICE AND PERMISSION

A portion of the disclosure of this patent document may contain materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever. The following notice shall apply to this document:Copyright © 2001, Microsoft Corp.

FIELD OF THE INVENTION

The present invention relates to systems and methods for optimizingparametrization schemes in connection with computer graphics. Moreparticularly, the present invention relates to systems and methods foroptimizing geometric stretch of a parametrization scheme.

BACKGROUND OF THE INVENTION

A progressive mesh (PM) representation encodes an arbitrary mesh as asimple base mesh M⁰ and a sequence of n refinement operations calledvertex splits. The PM defines an array {M⁰ . . . M^(n)} oflevel-of-detail (LOD) approximations, and supports geomorphs andprogressive transmission. Unlike multiresolution frameworks based onsubdivision, the meshes in a PM have irregular connectivities that canaccurately model sharp features (e.g., creases and corners) at allscales.

One challenge in the PM framework is handling texture maps. Hardwarerasterization features, including bump maps, normal maps, andmultitexturing, allow fine detail to be captured in texture imagesparametrized over the mesh. Processes that implicate sources fortextures include sampling detailed scanned meshes, evaluating solidtextures, ray tracing, and 3D painting. In this regard, there arevarious problems associated with parametrizing texture images over allmeshes in a PM sequence.

A single unfolding of an arbitrary mesh onto a texture image may createregions of high distortion, so generally a mesh must be partitioned intoa set of charts. Each chart is parametrized by a region of a texturedomain, and these parametrizations collectively form an atlas, anexemplary one of which is illustrated in FIG. 9C. For instance, severalschemes simplify a mesh and then construct a texture image chart overeach simplified face by sampling attributes, e.g., normals, from theoriginal mesh.

For a PM, one might consider re-using chart images defined on faces ofM⁰ for all meshes M¹ . . . M^(n). However, the problem is that a PM isgenerally not chart-compliant, in that its vertex splits can change thechart topology when applied indiscriminately near chart boundaries,thereby forcing parametric discontinuities. For example, the vertexsplit shown in FIG. 1B changes the adjacency of the three colored chartsof FIG. 1A, resulting in the discontinuous texture. Fortunately, it ispossible to construct a single atlas parametrization for the entire PMsequence. Chart-compliance can be obtained by first defining the chartson the original mesh, and then constraining a simplification sequence tocomply with those chart boundaries, as described by Cohen, Olano andManocha.

With respect to mesh partitioning, there have been several previoustechniques that have proposed methods for parametrizing meshes bypartitioning into charts. Krishnamurthy and Levoy describe aninteractive system in which the user manually lays out chart boundariesby tracing curves. Maillot et al. partition mesh faces according to abucketing of face normals. Eck et al. use a Voronoi-based partition.These last two algorithms make little effort to adapt charts to surfacegeometry, so the chart boundaries can hinder simplification, leading topoor LOD approximations.

The technmques of Lee et al. (“MAPS”) and Guskov et al. (“NormalMeshes”) map edges of the simplified base domain back to the originalmesh. While the resulting charts adapt to surface geometry, theirboundaries cut across faces of original mesh, requiring addition of newvertices and faces. For the applications described in MAPS and NormalMeshes, these additional vertices are only temporary, because the meshgeometry is subsequently resampled. However, the techniques of MAPS andNormal Meshes are not suited to generating a PM from a user-specifiedmesh, whose connectivity is often carefully optimized, because theimposition of new vertices is a drawback to such an application.

With respect to chart parametrization, several schemes have beenproposed to flatten surface regions to establish a parametrization.These schemes typically obtain the parametrization by minimizing anobjective functional. The main distinction between the functionals ishow they measure the distance of the parametrization from an isometry,which is a mapping preserving lengths and angles.

Maillot et al. base their metric on edge springs of nonzero rest length,where rest length corresponds to edge length on the surface. To ensurethat the parametrization is 1-to-1, i.e., to avoid parametric“buckling,” also called “face flipping,” they add an area-preservationterm to the metric. However, a downside to the technique of Maillot etal. is that when the texture domain boundary is fixed, it is unclear howedge rest-lengths should be scaled. Another downside is that theweighting between the edge springs and the area-preservation term mustbe adjusted to produce an embedding.

Eck et al. propose the harmonic map, which weights edge springsnon-uniformly. The weights can sometimes be negative, in which case anembedding is not guaranteed. Floater proposes a similar scheme with adifferent edge-spring weighting that guarantees embedding for convexboundaries. For either method, the parametrization can be found bysolving a linear system.

Lévy and Mallet combine orthogonality and isoparametric terms in theirmetric. To solve the resulting nonlinear optimization, they iterativelyfix one texture component, s or t, and solve for the other using alinear optimization. As with Lindstrom and Turk, a term is added, whichmust be sufficiently weighted to guarantee an embedding.

Horman and Greiner propose the MIPS parametrization, which roughlyattempts to preserve the ratio of singular values over theparametrization. However, the metric disregards absolute stretch scaleover the surface, with the result that small domain areas can map tolarge regions on the surface.

With respect to appearance preserving simplification, Cohen et al.introduce texture deviation as the appropriate measure of geometricaccuracy when simplifying textured meshes. The texture deviation betweena simplified mesh M^(i) and the original mesh M^(n) at a point p^(i) εM^(i) is defined as ∥p^(i)−p^(n)∥ where p^(n) is the point on M^(n) withthe same parametric location in the texture domain. Cohen et al. tracktexture deviation conservatively by storing a bounding error box at eachmesh vertex. They demonstrate results on parametric surfaces alreadyorganized into charts; however, the techniques of Cohen et al. do notbegin with an unparametrized mesh, and then seek to form an atlasparametrization that specifically minimizes texture deviation andstretch over all meshes in a PM.

Thus, it would be desirable to improve upon prior art techniques forproviding texture mapping in connection with parametrization schemes. Toimprove upon prior art techniques, it would be desirable to provide asystem that allows all meshes in the PM to share a common texture map.Furthermore, it would be desirable to create domains with straightboundaries between chart corners, unlike, for example, the approaches ofMaillot et al., Lévy and Mallet and Hormann and Greiner. It would bestill further desirable to provide systems and methods for texturemapping PMs that minimize geometric stretch and texture deviation. Itwould be still further desirable to substantially balance sampling rateseverywhere on the surface by optimizing the geometric stretch of aparametrization scheme. Moreover, it would be advantageous to provide ageneral solution that begins with an unparametrized mesh, and then formsan atlas parametrization that minimizes texture deviation and stretchover all meshes in a PM.

SUMMARY OF THE INVENTION

In view of the foregoing, the present invention provides systems andmethods for optimizing the geometric stretch of a parametrizationscheme. Given an arbitrary mesh, the systems and methods construct aprogressive mesh (PM) such that all meshes in the PM sequence share acommon texture parametrization. The systems and methods minimizegeometric stretch, i.e., small texture distances mapped onto largesurface distances, to balance sampling rates over all locations anddirections on the surface. The systems and methods also minimize texturedeviation, i.e., “slippage” error based on parametric correspondence, toobtain accurate textured mesh approximations. The technique(s) begin bypartitioning the mesh into charts using planarity and compactnessheuristics. Then, the technique(s) proceed by creating astretch-minimizing parametrization within each chart, and by resizingthe charts based on the resulting stretch. Then, the technique(s)simplify the mesh while respecting the chart boundaries. Next, theparametrization is re-optimized to reduce both stretch and deviationover the whole PM sequence. The charts may then be packed into a textureatlas for improved texture mapping in connection with a parametrizationscheme.

Other features and embodiments of the present invention are describedbelow.

BRIEF DESCRIPTION OF THE DRAWINGS

The file of this patent includes at least one drawing executed in color.Copies of this patent with color drawings will be provided by the UnitedStates Patent and Trademark Office upon request and payment of thenecessary fee.

The system and methods for optimizing geometric stretch of aparametrization scheme in accordance with the present invention arefurther described with reference to the accompanying drawings in which:

FIGS. 1A and 1B illustrate how a vertex split operation of aparametrization can result in discontinuous texture;

FIG. 2A is a block diagram representing an exemplary network environmenthaving a variety of computing devices in which the present invention maybe implemented;

FIG. 2B is a block diagram representing an exemplary non-limitingcomputing device in which the present invention may be implemented;

FIGS. 3A through 3G compare the metric of the present invention (FIGS.3F and 3G) with alternatives (FIGS. 3B through 3E) found in the priorart in relation to the exact reproduction of the image shown in FIG. 3A;

FIGS. 4A and 4B illustrate exemplary aspects of a half-edge collapseoperation utilized in accordance with the invention;

FIGS. 5A and 5B illustrate the results of the initial chart partitionoperation in accordance with the invention;

FIG. 6 illustrates exemplary aspects of a maximum deviation point, whichmay lie at a removed vertex or at an edge-edge intersection point;

FIG. 7 illustrates the effect of a pull-push algorithm utilized inaccordance with the invention on an exemplary atlas image;

FIGS. 8A and 8B depict error graphs of geometric stretch and deviationover an exemplary PM sequence for various techniques including thetechniques of the invention;

FIGS. 9A to 9D show an overview of the process of the optimization ofgeometric stretch of a parametrization scheme in accordance with theinvention;

FIGS. 10A to 10D show several mesh approximations in a PM sequence,where the texture image captures a normal map; and

FIGS. 11A to 11D illustrates the advantages of considering geometricstretch during parametrization in accordance with the invention byshowing the uniform surface sampling that results therefrom.

DETAILED DESCRIPTION OF THE INVENTION Overview

The present invention relates to systems and methods for optimizing thegeometric stretch of a parametrization scheme. In variousimplementations and embodiments, the systems and methods provide (1)algorithm(s) for partitioning a mesh into charts, which considerssimplification quality and does not alter the mesh, (2) a geometricstretch metric that uniformly penalizes undersampling everywhere overthe surface, (3) algorithm(s) for minimizing this stretch metric in theL² and L^(∞) norms, which can be used for both static meshes and PMs,(4) technique(s) for optimizing the parametrization to minimize bothgeometric stretch and texture deviation at all PM levels, withappropriate weighting of each mesh in M⁰ . . . M^(n), (5) technique(s)for ereating a PM representation with a consistent surfaceparametrization for all LODs and (6) technique(s) that form an atlasparametrization from an unparametrized mesh that minimizes texturedeviation and stretch over all meshes in a PM.

Exemplary Networked and Distributed Environments

One of ordinary skill in the art can appreciate that a computer or otherclient or server device can be deployed as part of a computer network,or in a distributed computing environment. In this regard, the presentinvention pertains to any computer system having any number of memory orstorage units, and any number of applications and processes occurringacross any number of storage units or volumes, which may be used inconnection with a parametrization process. The present invention mayapply to an environment with server computers and client computersdeployed in a network environment or distributed computing environment,having remote or local storage. The present invention may also beapplied to standalone computing devices, having programming languagefunctionality, interpretation and execution capabilities for generating,receiving and transmitting information in connection with remote orlocal services.

Distributed computing facilitates sharing of computer resources andservices by direct exchange between computing devices and systems. Theseresources and services include the exchange of information, cachestorage, and disk storage for files. Distributed computing takesadvantage of network connectivity, allowing clients to leverage theircollective power to benefit the entire enterprise. In this regard, avariety of devices may have applications, objects or resources that mayimplicate a parametrization process that may utilize the techniques ofthe present invention.

FIG. 2A provides a schematic diagram of an exemplary networked ordistributed computing environment. The distributed computing environmentcomprises computing objects 10 a, 10 b, etc. and computing objects ordevices 110 a, 110 b, 110 c, etc. These objects may comprise programs,methods, data stores, programmable logic, etc. The objects may compriseportions of the same or different devices such as PDAs, televisions, MP3players, televisions, personal computers, etc. Each object cancommunicate with another object by way of the communications network 14.This network may itself comprise other computing objects and computingdevices that provide services to the system of FIG. 2A. In accordancewith an aspect of the invention, each object 10 or 110 may contain anapplication that might request parametrization services.

In a distributed computing architecture, computers, which may havetraditionally been used solely as clients, communicate directly amongthemselves and can act as both clients and servers, assuming whateverrole is most efficient for the network. This reduces the load on serversand allows all of the clients to access resources available on otherclients, thereby increasing the capability and efficiency of the entirenetwork. Parametrization services in accordance with the presentinvention may thus be distributed among clients and servers, acting in away that is efficient for the entire network.

Distributed computing can help businesses deliver services andcapabilities more efficiently across diverse geographic boundaries.Moreover, distributed computing can move data closer to the point wheredata is consumed acting as a network caching mechanism. Distributedcomputing also allows computing networks to dynamically work togetherusing intelligent agents. Agents reside on peer computers andcommunicate various kinds of information back and forth. Agents may alsoinitiate tasks on behalf of other peer systems. For instance,intelligent agents can be used to prioritize tasks on a network, changetraffic flow, search for files locally or determine anomalous behaviorsuch as a virus and stop it before it affects the network. All sorts ofother services may be contemplated as well. Since graphical object(s)may in practice be physically located in one or more locations, theability to distribute parametrization services is of great utility insuch a system.

It can also be appreciated that an object, such as 110 c, may be hostedon another computing device 10 or 110. Thus, although the physicalenvironment depicted may show the connected devices as computers, suchillustration is merely exemplary and the physical environment mayalternatively be depicted or described comprising various digitaldevices such as PDAs, televisions, MP3 players, etc., software objectssuch as interfaces, COM objects and the like.

There are a variety of systems, components, and network configurationsthat support distributed computing environments. For example, computingsystems may be connected together by wireline or wireless systems, bylocal networks or widely distributed networks. Currently, many of thenetworks are coupled to the Internet, which provides the infrastructurefor widely distributed computing and encompasses many differentnetworks.

In home networking environments, there are at least four disparatenetwork transport media that may each support a unique protocol such asPower line, data (both wireless and wired), voice (e.g., telephone) andentertainment media. Most home control devices such as light switchesand appliances may use power line for connectivity. Data Services mayenter the home as broadband (e.g., either DSL or Cable modem) and areaccessible within the home using either wireless (e.g., HomeRF or802.11b) or wired (e.g., Home PNA, Cat 5, even power line) connectivity.Voice traffic may enter the home either as wired (e.g., Cat 3) orwireless (e.g., cell phones) and may be distributed within the homeusing Cat 3 wiring. Entertainment media may enter the home eitherthrough satellite or cable and is typically distributed in the homeusing coaxial cable. IEEE 1394 and DVI are also emerging as digitalinterconnects for clusters of media devices. All of these networkenvironments and others that may emerge as protocol standards may beinterconnected to form an intranet that may be connected to the outsideworld by way of the Internet. In short, a variety of disparate sourcesexist for the storage and transmission of data, and consequently, movingforward, computing devices will require ways of sharing data, such asdata accessed or utilized incident to the parametrization of graphicsobject(s) in connection with the present invention.

The Internet commonly refers to the collection of networks and gatewaysthat utilize the TCP/IP suite of protocols, which are well-known in theart of computer networking. TCP/IP is an acronym for “Transport ControlProtocol/Interface Program.” The Internet can be described as a systemof geographically distributed remote computer networks interconnected bycomputers executing networking protocols that allow users to interactand share information over the networks. Because of such wide-spreadinformation sharing, remote networks such as the Internet have thus fargenerally evolved into an open system for which developers can designsoftware applications for performing specialized operations or services,essentially without restriction.

Thus, the network infrastructure enables a host of network topologiessuch as client/server, peer-to-peer, or hybrid architectures. The“client” is a member of a class or group that uses the services ofanother class or group to which it is not related. Thus, in computing, aclient is a process, i.e., roughly a set of instructions or tasks, thatrequests a service provided by another program. The client processutilizes the requested service without having to “know” any workingdetails about the other program or the service itself. In aclient/server architecture, particularly a networked system, a client isusually a computer that accesses shared network resources provided byanother computer e.g., a server. In the example of FIG. 2A, computers110 a, 110 b, etc. can be thought of as clients and computer 10 a, 10 b,etc. can be thought of as the server where server 10 a, 10 b, etc.maintains the data that is then replicated in the client computers 110a, 110 b, etc.

A server is typically a remote computer system accessible over a remotenetwork such as the Internet. The client process may be active in afirst computer system, and the server process may be active in a secondcomputer system, communicating with one another over a communicationsmedium, thus providing distributed functionality and allowing multipleclients to take advantage of the information-gathering capabilities ofthe server.

Client and server communicate with one another utilizing thefunctionality provided by a protocol layer. For example,Hypertext-Transfer Protocol (HTTP) is a common protocol that is used inconjunction with the World Wide Web (WWW). Typically, a computer networkaddress such as a Universal Resource Locator (URL) or an InternetProtocol (IP) address is used to identify the server or client computersto each other. The network address can be referred to as a URL address.For example, communication can be provided over a communications medium.In particular, the client and server may be coupled to one another viaTCP/IP connections for high-capacity communication.

Thus, FIG. 2A illustrates an exemplary networked or distributedenvironment, with a server in communication with client computers via anetwork/bus, in which the present invention may be employed. In moredetail, a number of servers 10 a, 10 b, etc., are interconnected via acommunications network/bus 14, which may be a LAN, WAN, intranet, theInternet, etc., with a number of client or remote computing devices 110a, 110 b, 110 c, 110 d, 110 e, etc., such as a portable computer,handheld computer, thin client, networked appliance, or other device,such as a VCR, TV, oven, light, heater and the like in accordance withthe present invention. It is thus contemplated that the presentinvention may apply to any computing device in connection with which itis desirable to process graphical object(s).

In a network environment in which the communications network/bus 14 isthe Internet, for example, the servers 10 can be Web servers with whichthe clients 110 a, 110 b, 110 c, 110 d, 110 e, etc. communicate via anyof a number of known protocols such as HTTP. Servers 10 may also serveas clients 110, as may be characteristic of a distributed computingenvironment. Communications may be wired or wireless, where appropriate.Client devices 110 may or may not communicate via communicationsnetwork/bus 14, and may have independent communications associatedtherewith. For example, in the case of a TV or VCR, there may or may notbe a networked aspect to the control thereof. Each client computer 110and server computer 10 may be equipped with various application programmodules or objects 135 and with connections or access to various typesof storage elements or objects, across which files may be stored or towhich portion(s) of files may be downloaded or migrated. Any computer 10a, 10 b, 110 a, 110 b, etc. may be responsible for the maintenance andupdating of a database 20 or other storage element in accordance withthe present invention, such as a database or memory 20 for storinggraphics object(s) or intermediate graphics object(s) processedaccording to the invention. Thus, the present invention can be utilizedin a computer network environment having client computers 110 a, 110 b,etc. that can access and interact with a computer network/bus 14 andserver computers 10 a, 10 b, etc. that may interact with clientcomputers 110 a, 110 b, etc. and other devices 111 and databases 20.

Exemplary Computing Device

FIG. 2B and the following discussion are intended to provide a briefgeneral description of a suitable computing environment in which theinvention may be implemented. It should be understood, however, thathandheld, portable and other computing devices and computing objects ofall kinds are contemplated for use in connection with the presentinvention. While a general purpose computer is described below, this isbut one example, and the present invention may be implemented with athin client having network/bus interoperability and interaction. Thus,the present invention may be implemented in an environment of networkedhosted services in which very little or minimal client resources areimplicated, e.g., a networked environment in which the client deviceserves merely as an interface to the network/bus, such as an objectplaced in an appliance. In essence, anywhere that data may be stored orfrom which data may be retrieved is a desirable, or suitable,environment for operation of the parametrization techniques of theinvention.

Although not required, the invention can be implemented via an operatingsystem, for use by a developer of services for a device or object,and/or included within application software that operates in connectionwith the parametrization of graphics object(s). Software may bedescribed in the general context of computer-executable instructions,such as program modules, being executed by one or more computers, suchas client workstations, servers or other devices. Generally, programmodules include routines, programs, objects, components, data structuresand the like that perform particular tasks or implement particularabstract data types. Typically, the functionality of the program modulesmay be combined or distributed as desired in various embodiments.Moreover, those skilled in the art will appreciate that the inventionmay be practiced with other computer system configurations. Other wellknown computing systems, environments, and/or configurations that may besuitable for use with the invention include, but are not limited to,personal computers (PCs), automated teller machines, server computers,hand-held or laptop devices, multi-processor systems,microprocessor-based systems, programmable consumer electronics, networkPCs, appliances, lights, environmental control elements, minicomputers,mainframe computers and the like. The invention may also be practiced indistributed computing environments where tasks are performed by remoteprocessing devices that are linked through a communications network/busor other data transmission medium. In a distributed computingenvironment, program modules may be located in both local and remotecomputer storage media including memory storage devices, and clientnodes may in turn behave as server nodes.

FIG. 2B thus illustrates an example of a suitable computing systemenvironment 100 in which the invention may be implemented, although asmade clear above, the computing system environment 100 is only oneexample of a suitable computing environment and is not intended tosuggest any limitation as to the scope of use or functionality of theinvention. Neither should the computing environment 100 be interpretedas having any dependency or requirement relating to any one orcombination of components illustrated in the exemplary operatingenvironment 100.

With reference to FIG. 2B, an exemplary system for implementing theinvention includes a general purpose computing device in the form of acomputer 110. Components of computer 110 may include, but are notlimited to, a processing unit 120, a system memory 130, and a system bus121 that couples various system components including the system memoryto the processing unit 120. The system bus 121 may be any of severaltypes of bus structures including a memory bus or memory controller, aperipheral bus, and a local bus using any of a variety of busarchitectures. By way of example, and not limitation, such architecturesinclude Industry Standard Architecture (ISA) bus, Micro ChannelArchitecture (MCA) bus, Enhanced ISA (EISA) bus, Video ElectronicsStandards Association (VESA) local bus, and Peripheral ComponentInterconnect (PCI) bus (also known as Mezzanine bus).

Computer 110 typically includes a variety of computer readable media.Computer readable media can be any available media that can be accessedby computer 110 and includes both volatile and nonvolatile media,removable and non-removable media. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media includes both volatileand nonvolatile, removable and non-removable media implemented in anymethod or technology for storage of information such as computerreadable instructions, data structures, program modules or other data.Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CDROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can accessed by computer 110. Communication media typicallyembodies computer readable instructions, data structures, programmodules or other data in a modulated data signal such as a carrier waveor other transport mechanism and includes any information deliverymedia. The term “modulated-data signal” means a signal that has one ormore of its characteristics set or changed in such a manner as to encodeinformation in the signal. By way of example, and not limitation,communication media includes wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared and other wireless media. Combinations of any of the aboveshould also be included within the scope of computer readable media.

The system memory 130 includes computer storage media in the form ofvolatile and/or nonvolatile memory such as read only memory (ROM) 131and random access memory (RAM) 132. A basic input/output system 133(BIOS), containing the basic routines that help to transfer informationbetween elements within computer 110, such as during start-up, istypically stored in ROM 131. RAM 132 typically contains data and/orprogram modules that are immediately accessible to and/or presentlybeing operated on by processing unit 120. By way of example, and notlimitation, FIG. 2B illustrates operating system 134, applicationprograms 135, other program modules 136, and program data 137.

The computer 110 may also include other removable/non-removable,volatile/nonvolatile computer storage media. By way of example only,FIG. 2B illustrates a hard disk drive 141 that reads from or writes tonon-removable, nonvolatile magnetic media, a magnetic disk drive 151that reads from or writes to a removable, nonvolatile magnetic disk 152,and an optical disk drive 155 that reads from or writes to a removable,nonvolatile optical disk 156, such as a CD ROM or other optical media.Other removable/non-removable, volatile/nonvolatile computer storagemedia that can be used in the exemplary operating environment include,but are not limited to, magnetic tape cassettes, flash memory cards,digital versatile disks, digital video tape, solid state RAM, solidstate ROM, and the like. The hard disk drive 141 is typically connectedto the system bus 121 through an non-removable memory interface such asinterface 140, and magnetic disk drive 151 and optical disk drive 155are typically connected to the system bus 121 by a removable memoryinterface, such as interface 150.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 2B provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 110. In FIG. 2B, for example, hard disk drive 141 isillustrated as storing operating system 144, application programs 145,other program modules 146, and program data 147. Note that thesecomponents can either be the same as or different from operating system134, application programs 135, other program modules 136, and programdata 137. Operating system 144, application programs 145, other programmodules 146, and program data 147 are given different numbers here toillustrate that, at a minimum, they are different copies. A user mayenter commands and information into the computer 110 through inputdevices such as a keyboard 162 and pointing device 161, commonlyreferred to as a mouse, trackball or touch pad. Other input devices (notshown) may include a microphone, joystick, game pad, satellite dish,scanner, or the like. These and other input devices are often connectedto the processing unit 120 through a user input interface 160 that iscoupled to the system bus 121, but may be connected by other interfaceand bus structures, such as a parallel port, game port or a universalserial bus (USB). A graphics interface 182, such as Northbridge, mayalso be connected to the system bus 121. Northbridge is a chipset thatcommunicates with the CPU, or host processing unit 120, and assumesresponsibility for accelerated graphics port (AGP) communications. Oneor more graphics processing units (GPUs) 184 may communicate withgraphics interface 182. In this regard, GPUs 184 generally includeon-chip memory storage, such as register storage and GPUs 184communicate with a video memory 186. GPUs 184, however, are but oneexample of a coprocessor and thus a variety of coprocessing devices maybe included in computer 110. A monitor 191 or other type of displaydevice is also connected to the system bus 121 via an interface, such asa video interface 190, which may in turn communicate with video memory186. In addition to monitor 191, computers may also include otherperipheral output devices such as speakers 197 and printer 196, whichmay be connected through an output peripheral interface 195.

The computer 110 may operate in a networked or distributed environmentusing logical connections to one or more remote computers, such as aremote computer 180. The remote computer 180 may be a personal computer,a server, a router, a network PC, a peer device or other common networknode, and typically includes many or all of the elements described aboverelative to the computer 110, although only a memory storage device 181has been illustrated in FIG. 2B. The logical connections depicted inFIG. 2B include a local area network (LAN) 171 and a wide area network(WAN) 173, but may also include other networks/buses. Such networkingenvironments are commonplace in homes, offices, enterprise-wide computernetworks, intranets and the Internet.

When used in a LAN networking environment, the computer 110 is connectedto the LAN 171 through a network interface or adapter 170. When used ina WAN networking environment, the computer 110 typically includes amodem 172 or other means for establishing communications over the WAN173, such as the Internet. The modem 172, which may be internal orexternal, may be connected to the system bus 121 via the user inputinterface 160, or other appropriate mechanism. In a networkedenvironment, program modules depicted relative to the computer 110, orportions thereof, may be stored in the remote memory storage device. Byway of example, and not limitation, FIG. 2B illustrates remoteapplication programs 185 as residing on memory device 181. It will beappreciated that the network connections shown are exemplary and othermeans of establishing a communications link between the computers may beused.

Exemplary Distributed Computing Frameworks or Architectures

Various distributed computing frameworks have been and are beingdeveloped in light of the convergence of personal computing and theInternet. Individuals and business users alike are provided with aseamlessly interoperable and Web-enabled interface for applications andcomputing devices, making computing activities increasingly Web browseror network-oriented.

For example, MICROSOFT®'s .NET platform includes servers, building-blockservices, such as Web-based data storage and downloadable devicesoftware. Generally speaking, the NET platform provides (1) the abilityto make the entire range of computing devices work together and to haveuser information automatically updated and synchronized on all of them,(2) increased interactive capability for Web sites, enabled by greateruse of XML rather than HTML, (3) online services that feature customizedaccess and delivery of products and services to the user from a centralstarting point for the management of various applications, such ase-mail, for example, or software, such as Office NET, (4) centralizeddata storage, which will increase efficiency and ease of access toinformation, as well as synchronization of information among users anddevices, (5) the ability to integrate various communications media, suchas e-mail, faxes, and telephones, (6) for developers, the ability tocreate reusable modules, thereby increasing productivity and reducingthe number of programming errors and (7) many other cross-platformintegration features as well.

While exemplary embodiments herein are described in connection withsoftware residing on a computing device, one or more portions of theinvention may also be implemented via an operating system, applicationprogramming interface (API) or a “middle man” object between acoprocessor and requesting object, such that parametrization servicesmay be performed by, supported in or accessed via all of NET's languagesand services, and in other distributed computing frameworks as well.

Optimizing Geometric Stretch to Create Balanced Parametrization

The present invention relates to the optimization of geometric stretchin connection with a parametrization process. Given an arbitrary mesh,the systems and methods of the invention construct a progressive mesh(PM) such that all meshes in the PM sequence share a common textureparametrization. The systems and methods minimize geometric stretch,i.e., small texture distances mapped onto large surface distances, tobalance sampling rates over all locations and directions on the surface.The systems and methods also minimize texture deviation, i.e.,“slippage” error based on parametric correspondence, to obtain accuratetextured mesh approximations. The technique(s) begin by partitioning themesh into charts using planarity and compactness heuristics. Then, thetechnique(s) proceed by creating a stretch-minimizing parametrizationwithin each chart, and by resizing the charts based on the resultingstretch. Then, the technique(s) simplify the mesh while respecting thechart boundaries. Next, the parametrization is re-optimized to reduceboth stretch and deviation over the whole PM sequence. The charts maythen be packed into a texture atlas for improved texture mapping inconnection with a parametrization scheme.

To allow all meshes in the PM to share a common texture map, the presentinvention creates domains with straight boundaries between chartcorners. In accordance with the optimization of the present invention,two relevant processes for texture mapping PMs are optimized: geometricstretch and texture deviation. In this regard, geometric stretch andtexture deviation are minimized. The stretch metric utilized inaccordance with the invention balances sampling rates everywhere on thesurface, unlike previous techniques, thus proving to be an effectivegeneral solution when nothing is known in advance about the signal, orobject, being parametrized.

As intimated in the background, the invention addresses the followingproblem: given an arbitrary mesh, parametrize it onto a texture atlas,and create a PM sequence compliant with the atlas charts, whileminimizing geometric stretch and texture deviation.

With respect to minimizing geometric stretch, the parametrizationdetermines sampling density over the surface, but is constructed beforeknowing what texture map(s) will be applied. Therefore, the inventionprovides a balanced parametrization rather than one that samples finelyin some surface regions or directions while undersampling others. Thisis what is referred to herein as minimizing “geometric stretch.” Aconservative, local measure of how finely the parametrization samplesthe texture signal is the larger singular value of its Jacobian, whichmeasures how much a sampling direction in the texture domain isstretched on the mesh surface in the worst case. By minimizing thelargest geometric stretch across all domain points, the inventioncreates a balanced parametrization where no domain direction is toostretched and thus undersamples its corresponding mapped 3D direction.As will become apparent, FIGS. 3F, 3G, 11C and 11D show the minimizationof geometric stretch in accordance with the invention compared to FIGS.3A through 3E and FIGS. 11A and 11B.

With respect to minimizing texture deviation, traditional meshsimplification techniques measure geometric error by approximatingclosest-point distance. For textured surfaces, however, it is moreappropriate to use the stricter texture deviation error, which measuresgeometric error according to parametric correspondence. For a PM,texture deviation can be graphed as a function of mesh complexity, asshown in FIG. 8B. As shown by the graph, the invention lowers this graphcurve.

While the motivation for partitioning the surface into charts is toreduce geometric stretch, the presence of chart boundaries hinderssimplification quality since chart-compliance requires that theseboundaries appear as edges in all meshes including M⁰. In the extreme,if each face of M^(n) is made its own chart, stretch is zero, but nosimplification can occur. Hence, there exists a trade-off betweengeometric stretch and deviation.

Minimizing stretch and deviation is a difficult nonlinear problem overboth discrete and continuous variables. The discrete variables are themesh partition and the edge collapse sequence. The continuous variablesare the texture coordinates of the vertices. The techniques of theinvention in various embodiments set the discrete variables early, usingheuristics, and then proceed to optimize the continuous variables. Inone embodiment, the method includes the following steps: (1) partitionthe original mesh into charts (considering geometry), (2) form initialchart parametrizations (minimizing stretch), (3) resize the chartpolygons (based on stretch), (4) simplify the mesh (minimizing texturedeviation, creating PM), (5) optimize the parametrization (stretch &deviation over all PM), (6) pack the chart polygons (forming textureatlas) and (7) sample the texture images (using atlas parametrization).

The present invention defines a new geometric stretch metric. Tooptimize the ability of a parametrization to balance frequency contenteverywhere over the surface in every direction, the “geometric stretch”metric on triangle meshes is as follows:

Given a triangle T with 2D texture coordinates p₁,p₂,p₃,p_(i)=(s_(i),t_(i)), and corresponding 3D coordinates q₁,q₂,q₃, theunique affine mapping S(p)=S(s,t)=q is:S(p)=(<p,p ₂ ,p ₃ >q ₁ +<p,p ₃ ,p ₁ >q ₂ +<p,p ₁ ,p ₂ >q ₃)/<p ₁ ,p ₂ ,p₃>where <a,b,c> denotes area of triangle abc. Since the mapping is affine,its partial derivatives are constant over (s,t) and are given by:S _(s) =∂S/∂s=(q ₁(t ₂ −t ₃)+q ₂(t ₃ −t ₁)+q ₃(t ₁ −t ₂))/(2A)S _(t) =∂S/∂t=(q ₁(s ₃ −s ₂)+q ₂(s _(t) −s ₃)+q ₃(s ₂ −s ₁))/(2A)A=<p₁ ,p ₂ ,p ₃>=((s ₂ −s ₁)(t ₃ −t ₁)−(s ₃ −s ₁)(t ₂ −t ₁))/2The larger and smaller singular values of the Jacobian [S_(s),S_(t)] aregiven respectively by: $\begin{matrix}{\Gamma = \sqrt{{1/2}\left( {\left( {a + c} \right) + \sqrt{\left( {a - c} \right)^{2} + {4\quad b^{2}}}} \right)}} & {\max\quad{singular}\quad{value}} \\{\gamma = \sqrt{{1/2}\left( {\left( {a + c} \right) - \sqrt{\left( {a - c} \right)^{2} + {4b^{2}}}} \right)}} & {\min\quad{singular}\quad{value}}\end{matrix}$where a=S_(s)·S_(s), b=S_(s)·S_(t), and c=S_(t)·S_(t). The singularvalues Γ and γ represent the largest and smallest length obtained whenmapping unit-length vectors from the texture domain to the surface, i.e.the largest and smallest local “stretch”. Thus, in accordance with theinvention, two stretch norms are defined over triangle T:L ²(T)=√{square root over ((Γ²+γ²)/2)}=√{square root over ((a+c)/2)}, L^(∞)(T)=Γ.The norm L²(T) corresponds to the root-mean-square stretch over alldirections in the domain, and the worst-case norm L^(∞)(T) is thegreatest stretch, i.e., the maximum singular value. It is noted thatboth L²(T) and L^(∞)(T) increase to infinity as the parametrization of Tbecomes degenerate, since its parametric area A drops to zero. If thetriangle T flips parametrically, i.e., if A becomes negative, then bothL²(T) and L^(∞)(T) are defined to remain infinite.

In addition, two analogous norms over the surface of the entire meshM={T_(i)} are defined: $\begin{matrix}{{L^{2}(M)} = \sqrt{\sum\limits_{T_{i}\varepsilon\quad M}{\left( {L^{2}\left( T_{i} \right)} \right)^{2}{{A^{\prime}\left( T_{i} \right)}/{\sum\limits_{T_{i}\varepsilon\quad M}{A^{\prime}\left( T_{i} \right)}}}}}} \\{{L^{\infty}(M)} = {\max\limits_{T_{i}\varepsilon\quad M}{L^{\infty}\left( T_{i} \right)}}}\end{matrix}$where A′(T_(i)) is the surface area of triangle T_(i) in 3D. The L² normmeasures the overall ability of the parametrization to supporthigh-frequency textures, while L^(∞) measures its worst-case ability.The stretch values are normalized by scaling the texture domain so thatits area equals the surface area in 3D. Thus, 1.0 is a lower bound foreither norm on any parametrization. Alternately, stretch can benormalized without explicitly scaling the texture domain by multiplyingwith the factor:$\sqrt{\sum\limits_{T_{i}\varepsilon\quad M}{{A\left( T_{i} \right)}/{\sum\limits_{T_{i}\varepsilon\quad M}{A^{\prime}\left( T_{i} \right)}}}}.$

To minimize the nonlinear metrics L²(M) and L^(∞)(M), auniform-edge-spring solution begins the process, and then severaloptimization iterations are performed. Within each iteration, verticesare considered in decreasing order of neighborhood stretch. For eachvertex, a line search minimization is performed along a randomly chosensearch direction in the (s,t) parametric domain. Since the othervertices are held fixed, the stretch metric only needs local computationover the neighborhood. Since the metric is infinite for degenerate orflipped triangles, the line search is naturally constrained within thekernel of the vertex's neighborhood. In successive iterations, theconvergence tolerance is gradually decreased in the 1D line search usingthe schedule 1/i where i is the iteration number. For the L^(∞)optimization, better results are obtained by using our L² solution asits starting point.

FIGS. 3B through 3G compare the metric of the present invention (FIGS.3F and 3G) with alternatives (FIGS. 3B through 3E) found in the priorart. FIG. 3A illustrates an exact reproduction of the image upon whichthe techniques of FIGS. 3B to 3G operate. FIG. 3B illustrates theoperation of the Floater technique described in the background on FIG.3A. FIG. 3C illustrates the operation of the Hormann and Greinertechnique described in the background on FIG. 3A. FIG. 3D illustratesthe operation of the Maillot et al. technique described in thebackground on FIG. 3D. FIG. 3E illustrates the operation of the Cohen etal. technique described in the background on FIG. 3A. For each metric,the optimization procedure described above was utilized. Thus, in eachof FIGS. 3B to 3G, boundary vertices were fixed by arc-length. For eachparametrization, a 128×128 image was created in the texture domain bysampling a procedural 3D checkered pattern on the parametrized surface.For improved filtering, 4×4 supersampling was used. As can be seen inthe resulting textured models, parametrizations optimized using themetrics of the invention are better at capturing high-frequency detaileverywhere over the surface. In FIGS. 3B to 3D, there is a loss ofresolution on the ears of the cat where stretch error is high. For FIG.3D, the factor α is set weighting between edge-springs andarea-preservation to 0.5. Since the relative scale of 2D to 3D edgelengths is important in this metric, the 2D domain is uniformly scaledto have the same area as the 3D chart. The reported stretch norms of theinvention are infinite because the minimum solution exhibits buckling.Since the optimization method of the invention prevents face flipping,the result is parametrically degenerate triangles having infinitestretch. The area-preserving parametrization in FIG. 3E minimizes thebuckling term of FIG. 3D only. Although it has better spatialdistribution than FIGS. 3B to 3D, it is noted how the resulting imagesare undersampled in certain directions, causing directional blur on thechin, sides and ears. In turn, the techniques of FIG. 3F and FIG. 3G ofthe invention yield better results by comparison. FIGS. 3F and 3Gillustrate how undersampling is minimized everywhere on the surface inall directions with the invention, measuring how well theparametrization samples the signal all over the surface in alldirections.

The PM parametrization scheme in connection with various embodiments ofthe invention is now described in more detail. First, however, somedefinitions and assumptions are introduced: Let a chart corner be anyvertex adjacent to three or more charts, and let a chart boundary be thepath of edges separating charts between two corners. Let theneighborhood of a vertex be defined as the ring of faces adjacent to thevertex.

The PM of the invention is based on the half-edge collapse operation(v₁, v₂)→v₁ which affects the neighborhood of v₂ as shown in FIG. 4B inrelation to FIG. 4A and leaves the position and attributes of v₁unchanged. The present invention preferably utilizes the half-edgecollapse as opposed to the full-edge collapse to avoid writes to thevertex buffer during runtime LOD changes, although the invention is notso limited. Therefore, (s,t) texture coordinates at any vertex must bethe same at all LOD levels. Since a vertex on a chart boundary hasdifferent (s,t) coordinates on each chart, these must be stored at thecorners of mesh faces.

To create a texture atlas over a PM, the following constraints areenforced in accordance with the present invention: (1) mesh faces cannotspan more than one chart, since it is impractical to specify and renderdisjoint pieces of texture over any single triangle and (2) chartboundaries must be straight in the parametric domain, since each chartboundary is generally simplified to a single edge in the PM base mesh.These constraints restrict the partition of the mesh into charts and themesh simplification sequence, as described below.

With respect to partitioning meshes into charts, the invention firstincludes partitioning the mesh into a set of charts, i.e., regions withdisk-like topology. Ideally, one could simultaneously search over thediscrete space of possible chart decompositions and the continuous spaceof parametrizations allowed by each decomposition. However, this is nota very practical approach. Instead, the present invention firstpartitions the mesh using a greedy chart-merging approach that issimilar to simplification schemes based on the greedy growth of“superfaces.”

In this regard, initially, each face is assigned to be its own chart.For each pair of adjacent charts, the invention considers the operationof merging the two charts into one, and enters this candidate operationinto a priority queue according to a computed cost, i.e., the mergeoperation is assigned a cost that measures both its planarity andcompactness. In one embodiment, planarity is measured as themean-squared distance of the chart to the best-fitting plane through thechart, defined as a continuous surface integral, unlike prior arttechniques that evaluate planarity only at the vertices. Compactness ismeasured simply as the squared perimeter length.

The merge operation is then iteratively applied with lowest cost, andcosts of neighboring candidate merge operations are updated. The processends when the cost exceeds a user-specified threshold.

A chart merge operation is disallowed if it results in any chart withfewer than 3 corners. It is also disallowed if the boundary between thenew chart and any adjacent chart consists of more than one connectedcomponent, e.g., one isolated vertex and one path of edges. Thisconstraint also guarantees that charts remain homeomorphic to discs.

Once the charts are formed, they define the set of chart cornervertices. Note that these corner vertices in M^(n) appear as vertices inthe base mesh M⁰ due to the constrained half-edge collapses. Therefore,with the invention, it is desirable that each chart boundary be closelyaligned with the straight line segment between its adjacent two corners,so as not to be a limiting factor in the simplification quality. Eachboundary is straightened by computing a shortest path over mesh edges,constrained in that there should be no intersection with other chartboundaries. Results of the initial chart partition, and the subsequentboundary straightening are shown in FIG. 5B in relation to FIG. 5A. Itis noted that chart boundaries align with important features in themesh. FIG. 5A shows the initial chart partitions, and the correspondingfigures in FIG. 5B show the results of chart boundary optimization.

With respect to forming initial chart parametrizations, once the chartboundaries are defined in M^(n), the invention creates an initialparametrization of each chart onto a 2D polygon. The 2D polygon boundaryis defined to be a convex polygon with vertices on a circle, where thelength of each polygon edge is proportional to the arc-length of thecorresponding chart boundary in 3D. The polygon is initially scaled tohave unit area. Within each chart, the interior vertices areparametrized by minimizing the L²(M) stretch metric, using the algorithmdescribed above in connection with the geometric stretch metric of theinvention. It is noted that boundary vertices could be optimized aswell.

With respect to resizing chart polygons, once chart parametrizations onM^(n) are formed, the invention determines how much relative space eachchart should be granted in the texture domain. For each chart, theinvention computes L²(M^(n)chart), the root mean square (RMS) stretchover the chart, and uses that value to uniformly resize the chart whilepreserving its shape. Although the relative chart sizes have no effecton simplification described below, they do affect E(PM) in the final PMoptimization described below simplification.

With respect to simplifying the mesh, given the initial chartparametrizations, the invention simplifies the mesh to define a PM.During simplification, texture deviation is minimized. In accordancewith the invention, edge collapses are selected that minimize texturedeviation by using a priority queue. To enforce chart compliance, edgecollapse (v₁,v₂)→v₁ in M^(i+1)→M^(i) are disallowed if vertex v₂ is achart corner (to preserve corners), or if v₂ is on a chart boundary andedge (v₁,v₂) is not on a chart boundary (to preserve boundarystraightness). In addition, the creation of parametrically flipped ordegenerate triangles is prevented.

To measure texture deviation for each candidate edge collapse, ratherthan using conservative bounds, the invention measures the incrementaltexture deviation d(M^(i+1),M^(i)) between the two meshes. It is notedthat his measurement is based on a heuristic akin to the “memoryless”error that has proven effective for geometric simplification. Themaximum deviation between M^(i+1) and M^(i) is known to lie either atthe removed vertex v₂ or at an edge-edge intersection point in theparametric neighborhood, e.g., the red points RPs shown in FIG. 6. Inthis regard, it has empirically shown that the incremental deviationheuristic works well by comparing the heuristic to a slow simplificationthat orders edge collapses using the true deviation error, i.e.,deviation between M^(i) and M^(n).

With respect to the optimization of chart parametrizations, havingdetermined the PM simplification sequence, the invention re-optimizesthe chart parametrizations to minimize stretch and deviation on theentire sequence M⁰ . . . M^(n). The nonlinear optimization algorithmutilized in accordance with the invention follows the strategy of movingvertices of M^(n) one-by-one in the parametric domain as described abovein connection with the geometric stretch metric and the formation ofinitial chart parametrizations, but using a different objectivefunction.

This objective function is a weighted sum of the geometric stretch anddeviation on all meshes M⁰ . . . M^(n) as follows:E(PM)=Σ_(i=0 . . . n)ψ(i)[λL ²(M ^(i))²+(1−λ)d(M ^(i) ,M ^(n))² /A′(M^(n))]where L²(M^(i)) is the normalized average stretch of M^(i) describedabove computed using the resized charts described above with respect tothe resizing of chart polygons, d(M^(i),M^(n)) is its texture deviation,the parameter 0≦λ≦1 is used to weight stretch error relative todeviation error, and ψ(i) is the relative weight assigned to each LODmesh in the sequence. Dividing by the mesh surface area A′(M^(n)) makesthe second term scale-invariant like the first term.

A model will now be introduced for setting the relative weight ψ(i)assigned to each mesh M^(i), consisting of two factors: usage and scale.Depending on the application, other weighting schemes could be used,without changing the optimization method.

In LOD applications, coarser meshes are likely to be usedproportionately more often. For example, meshes with 10-100 faces arelikely to be used more than those with 900-990 faces. Thus, a reasonablemodel for usage probability is a uniform distribution over a logarithmicscale of model complexity, e.g., meshes with 10-100 faces are as equallylikely as meshes with 100-1000 faces. This distribution is obtainedusing the factor 1/|M^(i)| where |M| is the number of vertices in M.

The fact that coarser meshes are typically used when the object isfarther away reduces the screen-space scale of their deviation andstretch. For a smooth spherical surface, texture deviation varies as1/|M|². Since LOD algorithms attempt to maintain a constant screen-spaceerror, deviation and, stretch in model space should therefore bedown-weighted for coarser meshes using the weighting factor |M^(i)|² inψ(i).

To optimize the texture coordinates of a given vertex v, theoptimization algorithm of the invention repeatedly evaluates E.Computing E using the above sum over all meshes would be computationallyexpensive. Fortunately, the neighborhood of v changes only a few timeswithin the sequence of meshes, generally O(log |M^(n)|) times. Thus, theinvention considers E only on each refinement neighborhood M^(i)→M^(i+1)of which v is a member. For each vertex v, the relevant refinementneighborhoods are gathered as a list during a coarse-to-fine preprocesstraversal of the PM.

Since the refinement neighborhoods adjacent to a vertex v have anapproximately logarithmic distribution over the PM sequence, we canaccount for the usage factor by summing the stretch and deviation onthese refinement neighborhoods. Therefore, we weight the error over eachsuch neighborhood by ψ′(i)=|M^(i)|² to account for the remaining scalefactor.

The following pseudo-code is an exemplary and non-limitingimplementation of the technique(s) described above:

// Optimize parametrization over whole PM sequence. procedureparametrize_pm( ) gather_refinement_neighborhoods( ) // coarse-to-finetraversal repeat v = some_vertex_in_mesh(M^(n)) optimize vertex(v) untilconvergence // Optimize the parametrization param(v) of vertex vprocedure optimize_vertex(vertex v) repeat vector dir =random_search_direction( )  // in 2D domain // perform line searchminimization repeat select float t  // e.g. using binaiy line searchparam(v) = param(v) + dir * t // perturb parametrization of v untilerror_over_PM(v) is minimized until convergence // Sum of errorsaffected by param(v) in all meshes M⁰...M^(n). functionerror_over_PM(vertex v) error = 0 for (vertex w inrefinement_neighborhoods(v)) error += error_over neighborhood(w, v)return error // Error due to v in neighborhood of w (where w is firstintroduced) function error_over_neighborhood(vertex w, vertex v) returnψ'level(w)) [λ * stretch_error(w, original neighbors(w), v) + (1-λ) *deviation error(w, original neighbors(w), v) / A′(M^(n))]

As described above in connection with mesh simplification, the inventionapproximates the deviation error d(M^(i),M^(n)) with the incrementaldeviation error d(M^(i),M^(i+1)). Because the stretch metric of theinvention is defined to be infinite when a face is flipped in theparameter domain, stretch minimization prevents parametric flipping inall meshes. One further non-limiting detail is that the inventionoptimizes the parametrization of vertices along chart boundaries. Sincethese vertices have texture coordinates in two adjacent charts, theinvention considers the refinement neighborhoods in both chartssimultaneously. Specifically, parametrizations are constrained to remainon the boundaries, and optimizations take place over shared barycentriccoordinates along the boundary to prevent “parametric cracks.”

With respect to packing chart polygons, since the above-describedoptimization modifies the parametrization, a final chart resizing stepis performed in accordance with the invention.

Thus, the next step is to pack the resized charts into a rectangulartexture image. In the context of texture mapping, various heuristicshave been presented for the special case of packing 3-sided charts;however, the chart boundaries of the invention advantageously can bearbitrary polygons. In this regard, the general problem is known as theNP-hard pants packing problem.

The problem is simplified by the invention by conservativelyapproximating each chart polygon with the least-area rectangle thatencloses it. This rectangle is found efficiently by considering eachedge of the polygon's convex hull. Fortunately, the chart polygons ofthe invention are reasonably shaped, so the rectangle approximation isnot too costly. The chart is rotated to align the long axis of therectangle with the vertical direction. The problem then becomes that ofrectangle packing, which is still NP-hard. The invention provides asimple heuristic for the problem, which works as follows.

Rectangles are sorted by height. In order of decreasing height,rectangles are placed sequentially into rows in alternatingleft-to-right and right-to-left order as shown in FIG. 9C. Throughbinary search, the invention optimizes over the texture width such thatthe packing minimizes the area of the enclosing square.

When the desired texture sampling density is later determined, a onetexel gap is left between adjacent charts. FIGS. 9A through 11Dillustrate results of the chart packing efficiency of the invention.

With respect to sampling texture images, the packed charts define atexture atlas for the surface. In accordance with the invention, theatlas is used to sample attributes from the surface M^(n) into thetexture domain, at the 2D grid of texel locations. For improvedfiltering, the invention supersamples the attributes using a 4×4 boxfilter. FIGS. 9A through 11D illustrate results of sampling colors andnormals.

If the highest frequency f of the attribute function over the surfacemesh is known, the stretch-based scale of the texture atlas makes itpossible to estimate the required 2D grid sampling density. With thecharts resized as described previously herein, the 2D grid spacingshould be set no more than 1/(2f).

In general, schemes that pack multiple charts into a single textureimage may give rise to mip-mapping artifacts, since coarser mip-maplevels average together spatially disjoint charts. The most immediateartifact is that chart boundaries are revealed if the inter-chart areais left unpainted, e.g., black. To mitigate this, the invention appliesa pull-push algorithm to fill in these unsampled regions with reasonablevalues. As an example, the effect on the atlas image from FIG. 9C isshown in FIG. 7.

FIGS. 8A and 8B thus compare graphs of geometric stretch and deviation,respectively, for meshes in a PM using various parametrization schemes.The curve labeled “uniform” corresponds to uniform edge-springparametrization followed by simplification minimizing texture deviation.The curve labeled “min-stretch param.” replaces the initialparametrization with the techniques of the invention described above inconnection with the section regarding initializing chartparametrizations. As is evident in FIG. 8A, parametric stretch isreduced for the finest mesh M^(n). It is noted that this difference canoften be more significant as shown in Table 1 below. The curve mayappear bumpy because stretch is ignored during simplification. Finally,the curve labeled “min-stretch+optimiz.” adds the parametrizationoptimization of the invention described above. It is noted that theinvention improves stretch at lower LODs, while also improving texturedeviation over the whole range.

As mentioned, results of the various techniques presented herein arepresented in FIGS. 9A through 11D. FIGS. 9A to 9D show an overview ofthe process of the invention. First, the original mesh is partitionedinto charts, establishing a stretch-minimizing parametrization on eachchart, and then the mesh is simplified while minimizing texturedeviation. With the resulting PM sequence M⁰ . . . M^(n), theparametrization is further optimized to reduce stretch and deviation inall meshes. Finally, the invention packs the charts into an atlas, andfills the atlas with texture samples from M^(n). FIGS. 9A to 9Dillustrate an example result where the texture image captures pre-shadedcolors from the original mesh M^(n). Although only the textured basemesh is shown, the same texture atlas can be used on all other meshes M¹. . . M^(n) in the PM.

FIGS. 10A to 10D show several mesh approximations in a PM sequence,where the texture image captures a normal map. Because the PM meshes canhave irregular connectivities, they quickly converge to good geometricapproximations. Hence the figure shows LOD meshes with relatively lowface-counts, compared to the original mesh of nearly 97,000 faces.

FIGS. 11A to 11D illustrate that ignoring geometric stretch duringparametrization results in non-uniform surface sampling, which becomesapparent as loss of detail over regions of high stretch distortion.

Table 1 below provides results on the efficiency of the parametrizationin reducing the required texture memory of the bunny of FIGS. 9A to 9Dand of the parasaur, horse and hand of FIGS. 5A and 5B. Stretchefficiency is the total surface area in 3D divided by the total chartarea in 2D, Σ_(T)A′(T)/Σ_(T)A(T), given that charts are resized asdescribed herein. It is less than unity if some surface regions aresampled more than necessary, i.e., if geometric stretch is not uniformeverywhere and in every direction. Packing efficiency is the sum ofchart areas in 2D divided by the rectangular texture domain area. It isless than unity due to two factors: the enclosure of chart polygons intorectangles, and the wasted space between the packed rectangles. Textureefficiency is the product of stretch and packing efficiencies, or totalsurface area divided by texture domain area.

TABLE 1 Quantitative results. Models bunny parasaur horse hand # facesin M^(n) 69,630 43,866 96,956 60,856 # vertices in M^(n) 34,817 21,93548,480 30,430 # charts 75 75 120 60 # faces in M⁰ 288 298 470 230 #vertices in M⁰ 146 151 237 117 (stretch 0.41 0.001 0.38 0.07 efficiencywith uniform parametrization) Stretch 0.60 0.40 0.55 0.46 efficiencyintra-rectangle 0.77 0.71 0.77 0.76 efficiency rectangle- 0.87 0.89 0.910.82 packing effic. Packing 0.67 0.63 0.70 0.62 efficiency Texture 0.400.25 0.38 0.29 efficiency

A 1-texel gutter is required between texture charts in the texturedomain. The overhead of these gutters depends on the resolution assignedto the texture. The packing efficiencies reported in Table 1 ignore thisoverhead, and therefore assume a reasonably high sampling rate.

It is noted that stretch efficiency can be improved by partitioning thesurface into more charts, but this increases the complexity of thecoarsest LOD mesh, and may lower overall texture efficiency due to theadditional gutter area. The stretch-minimizing parametrization of theinvention allows larger charts with fewer undersampling artifacts.

Thus, the present invention presents a scheme for defining a textureatlas parametrization over the PM representation of an arbitrary mesh.This atlas permits the same texture image(s) to be used for all LOD meshapproximations in the PM sequence. In forming the parametrization, theinvention optimizes for both geometric stretch and deviation on allmeshes in the sequence. Such optimizing according to the stretch metricof the invention creates a balanced parametrization that preventsundersampling at all locations and along all directions.

As mentioned above, while exemplary embodiments of the present inventionhave been described in connection with various computing devices andnetwork architectures, the underlying concepts may be applied to anycomputing device or system in which it is desirable to reconstructsignals from point samples with minimal error. Thus, the techniques forproviding improved signal processing in accordance with the presentinvention may be applied to a variety of applications and devices. Forinstance, the algorithm(s) of the invention may be applied to theoperating system-of a computing device, provided as a separate object onthe device, as part of another object, as a downloadable object from aserver, as a “middle man” between a device or object and the network, asa distributed object, etc. While exemplary programming languages, namesand examples are chosen herein as representative of various choices,these languages, names and examples are not intended to be limiting. Oneof ordinary skill in the art will appreciate that there are numerousways of providing object code that achieves the same, similar orequivalent parametrization achieved by the invention.

The various techniques described herein may be implemented in connectionwith hardware or software or, where appropriate, with a combination ofboth. Thus, the methods and apparatus of the present invention, orcertain aspects or portions thereof, may take the form of program code(i.e., instructions) embodied in tangible media, such as floppydiskettes, CD-ROMs, hard drives, or any other machine-readable storagemedium, wherein, when the program code is loaded into and executed by amachine, such as a computer, the machine becomes an apparatus forpracticing the invention. In the case of program code execution onprogrammable computers, the computing device will generally include aprocessor, a storage medium readable by the processor (includingvolatile and non-volatile memory and/or storage elements), at least oneinput device, and at least one output device. One or more programs thatmay utilize the signal processing services of the present invention,e.g., through the use of a data processing API or the like, arepreferably implemented in a high level procedural or object orientedprogramming language to communicate with a computer system. However, theprogram(s) can be implemented in assembly or machine language, ifdesired. In any case, the language may be a compiled or interpretedlanguage, and combined with hardware implementations.

The methods and apparatus of the present invention may also be practicedvia communications embodied in the form of program code that istransmitted over some transmission medium, such as over electricalwiring or cabling, through fiber optics, or via any other form oftransmission, wherein, when the program code is received and loaded intoand executed by a machine, such as an EPROM, a gate array, aprogrammable logic device (PLD), a client computer, a video recorder orthe like, or a receiving machine having the signal processingcapabilities as described in exemplary embodiments above becomes anapparatus for practicing the invention. When implemented on ageneral-purpose processor, the program code combines with the processorto provide a unique apparatus that operates to invoke the functionalityof the present invention. Additionally, any storage techniques used inconnection with the present invention may invariably be a combination ofhardware and software.

While the present invention has been described in connection with thepreferred embodiments of the various figures, it is to be understoodthat other similar embodiments may be used or modifications andadditions may be made to the described embodiment for performing thesame function of the present invention without deviating therefrom. Forexample, while exemplary network environments of the invention aredescribed in the context of a networked environment, such as a peer topeer networked environment, one skilled in the art will recognize thatthe present invention is not limited thereto, and that the methods, asdescribed in the present application may apply to any computing deviceor environment, such as a gaming console, handheld computer, portablecomputer, etc., whether wired or wireless, and may be applied to anynumber of such computing devices connected via a communications network,and interacting across the network. Furthermore, it should be emphasizedthat a variety of computer platforms, including handheld deviceoperating systems and other application specific operating systems arecontemplated, especially as the number of wireless networked devicescontinues to proliferate. Still further, the present invention may beimplemented in or across a plurality of processing chips or devices, andstorage may similarly be effected across a plurality of devices.Therefore, the present invention should not be limited to any singleembodiment, but rather should be construed in breadth and scope inaccordance with the appended claims.

1. A method for optimizing geometric stretch of a parametrization schemein connection with computer graphics, comprising: parametrizing a meshutilizing a geometric stretch metric, wherein the geometric stretchmetric is used to measure a plurality of geometric stretch metric valuescorresponding to how much undersampling exists for different points onthe surface of the mesh in accordance with spatial relationships of themesh, and wherein said parametrizing includes partitioning anunparametrized mesh into a plurality of charts, and wherein saidpartitioning includes considering geometry of the mesh; and minimizingthe plurality of geometric stretch metric values to minimizeundersampling over all points on the surface of the mesh.
 2. A methodaccording to claim 1, wherein the plurality of geometric stretch metricvalues are measured by integrating a pointwise undersampling metric overthe surface area of the mesh.
 3. A method according to claim 2, whereinthe geometric stretch metric is based on at least one of a L² and L^(∞)norm, which correspond to the root mean square stretch over alldirections in the domain and the maximum singular value obtained whenmapping unit-length vectors from the texture domain to the surface,respectively.
 4. A method according to claim 1, wherein said minimizingincludes updating individual vertex positions of the chart using linesearches.
 5. A method according to claim 1, wherein the spatialrelationships include x, y and z coordinate spatial relationships of themesh.
 6. A method according to claim 1, wherein said minimizing includesresizing polygons of said plurality of charts, and wherein said resizingis based upon geometric stretch.
 7. A method according to claim 6,further comprising: forming a texture atlas, wherein said formingincludes performing a second resizing of the polygons of said pluralityof charts and packing the polygons of said plurality of charts to definesaid texture atlas.
 8. A method according to claim 7, further comprisingsampling texture images based upon said texture atlas.
 9. A methodaccording to claim 8, wherein said sampling includes applying apull-push algorithm to fill in unsampled regions.
 10. A computerreadable medium having stored thereon a plurality of computer-executableinstructions for performing the method of claim
 1. 11. A modulated datasignal carrying computer executable instructions for performing themethod of claim
 1. 12. At least one of a coprocessing device and acomputing device comprising means for performing the method of claim 1.13. A method for optimizing geometric stretch of a parametrizationscheme in connection with computer graphics, comprising: receiving amesh; and generating a progressive mesh (PM) sequence from said meshutilizing a geometric stretch metric, wherein the geometric stretchmetric measures a plurality of geometric stretch metric valuescorresponding to how much undersampling exists for different points onthe surface of a mesh of said PM sequence in accordance with spatialrelationships of the mesh; and wherein: said generating includespartitioning the mesh into a plurality of charts; and said partitioningincludes considering geometry of the mesh.
 14. A method according toclaim 13, further including minimizing the plurality of geometricstretch metric values to minimize undersampling at any point on thesurface of the mesh of said PM sequence.
 15. A method according to claim14, wherein said minimizing includes updating individual vertexpositions of the chart using line searches.
 16. A method according toclaim 13, wherein the plurality of geometric stretch metric values aremeasured by integrating a pointwise undersampling metric over thesurface area of the mesh.
 17. A method according to claim 14, whereinthe geometric stretch metric is based on at least one of a L² and L^(∞)norm, which correspond to the root mean square stretch over alldirections in the domain and the maximum singular value obtained whenmapping unit-length vectors from the texture domain to the surface,respectively.
 18. A method according to claim 13, wherein the spatialrelationships include x, y and z coordinate spatial relationships of themesh.
 19. A method according to claim 14, wherein said minimizing isbased upon minimization of texture deviation and geometric stretch overall meshes in the PM sequence.
 20. A method according to claim 13,wherein said generating further includes forming an initialparametrization of said plurality of charts, wherein said formingincludes minimizing geometric stretch.
 21. A method according to claim20, wherein said generating further includes resizing polygons of saidplurality of charts, and wherein said resizing is based upon geometricstretch.
 22. A method according to claim 21, wherein said minimizingfurther includes simplifying the mesh after said resizing to produce aninitial PM sequence, and wherein said simplifying includes minimizingtexture deviation.
 23. A method according to claim 22, wherein saidminimizing further includes optimizing said initial PM sequence toconstruct said PM sequence, and wherein said optimizing includesminimizing both geometric stretch and texture deviation over all of saidinitial PM sequence.
 24. A method according to claim 21, furthercomprising: forming a texture atlas based upon said PM sequence, whereinsaid forming includes performing a second resizing of the polygons ofthe plurality of charts and packing the polygons of the plurality ofcharts to define said texture atlas.
 25. A method according to claim 24,further comprising sampling texture images based upon said textureatlas.
 26. A method according to claim 25, wherein said samplingincludes applying a pull-push algorithm to fill in unsampled regions.27. A computer readable medium having stored thereon a plurality ofcomputer-executable instructions for performing the method of claim 13.28. A modulated data signal carrying computer executable instructionsfor performing the method of claim
 13. 29. At least one of acoprocessing device and a computing device comprising means forperforming the method of claim
 13. 30. A method for optimizing geometricstretch of a parametrization scheme in connection with computergraphics, comprising: partitioning a mesh into a plurality of chartsutilizing a geometric stretch metric, wherein: said partitioningincludes considering geometric of the mesh; and the geometric stretchmetric measures a plurality of geometric stretch metric valuescorresponding to how much undersampling exists for different points onthe surface of the mesh in accordance with spatial relationships of themesh.
 31. A method according to claim 30, further including minimizingthe plurality of geometric stretch metric values to minimizeundersampling at any point on the surface of the mesh of said PMsequence.
 32. A method according to claim 31, wherein said minimizingincludes updating individual vertex positions of the chart using linesearches.
 33. A method according to claim 30, wherein the plurality ofgeometric stretch metric values are measured by integrating a pointwiseundersampling metric over the surface area of the mesh.
 34. A methodaccording to claim 33, wherein the geometric stretch metric is based onat least one of a L² and L^(∞) norm, which correspond to theroot-mean-square stretch over all directions in the domain and themaximum singular value obtained when mapping unit-length vectors fromthe texture domain to the surface, respectively.
 35. A method accordingto claim 30, wherein the spatial relationships include x, y and zcoordinate spatial relationships of the mesh.
 36. A method according toclaim 30, wherein said partitioning includes scaling at least one chartarea of the plurality of charts relative to at least one other chartarea of the plurality of charts.
 37. A method according to claim 30,wherein said partitioning includes resizing polygons of said pluralityof charts, and wherein said resizing is based upon geometric stretch.38. A computer readable medium having stored thereon a plurality ofcomputer-executable instructions for performing the method of claim 30.39. A modulated data signal carrying computer executable instructionsfor performing the method of claim
 30. 40. At least one of acoprocessing device and a computing device comprising means forperforming the method of claim 30.